Still imaging and video devices have become a significant part of consumer electronics. Digital cameras, digital camcorders, and video cellular phones are common, and many other new devices are being introduced into and evolving in the market continually. Advances in large resolution charge-coupled device (CCD) and complementary metal-oxide semiconductor (CMOS) image sensors, together with the availability of low-power, low-cost digital signal processors (DSPs), has led to the development of digital cameras with both high resolution (e.g., a five-megacell image sensor with a 2560×1920 cell array) still image and audio/video clip capabilities. In fact, high resolution digital cameras provide quality close to that offered by traditional 35 mm film cameras.
Digital cameras typically contain an “image pipeline” for digital camera control and image processing. Among other things, the image pipeline performs automatic focus, automatic exposure, and automatic white balancing, color filter array (CFA) interpolation, gamma correction, color space conversion, and Joint Photographic Experts Group/Motion Picture Experts Group compression/decompression (JPEG for single images and MPEG for video clips).
The typical color CCD/CMOS sensor consists of a rectangular array of photosites (cells) covered by the CFA; each cell of the CCD/CMOS sensor is covered by a color filter element of the CFA. Most CFAs employ a Bayer pattern of red, green and blue filters. As those skilled in the art understand, a Bayer pattern is a 2×2 array of color cells, a red cell, two green cells and a blue cell. For this reason, the Bayer pattern is sometimes referred to as GRGB. The two green cells are at the diagonal positions. In the horizontal direction, each line has either “R” and “Gr” or “Gb” and “B” where Gr is a green cell in a line with red cells, and Gb is a green cell in a line with blue cells. Green cells form a checkerboard pattern, and R or B cells fill the rest of each line.
The sensor output is an image with each cell amplitude a measure of the intensity of the corresponding filter color of the cell. The CFA interpolation provides the two other color amplitudes for each cell to give the full-color image of the input scene. The image sensor is the key component that determines the basis of the overall image quality. Among the various sensors, CCD sensors and CMOS sensors are the most widely used ones, and the percentage of CMOS sensors is growing.
Although CMOS sensors consume less power and have other advantages over CCD sensors, they suffer several disadvantages as well. One of the disadvantages is that the gain of Gb cells differs from that of Gr cells. This gives rise to so-called Gr-Gb gain imbalance.
Gr-Gb gain imbalance is not only dependent on geometrical location, but also is affected by surrounding R and B cells. Gr-Gb gain imbalance results in different hues of green being rendered given the same input light, which produces image artifacts, namely false textures. Therefore, this gain imbalance needs to be removed.
The straightforward way to remove Gr-Gb gain imbalance is to average green cells with the surrounding four other green cells. This is equivalent to low-pass filtering the green checkerboard. This removes Gr-Gb gain imbalance very well, but it also loses edge sharpness.
Another way to remove Gr-Gb gain imbalance is to use CFA interpolation methods that are insensitive to Gr-Gb gain imbalance. For example, some CFA interpolation methods interpolate RGB colors at the position in between Gb and Gr cells. This way the resulting green amplitude is close to the average of Gb and Gr. This method removes all or a large part of Gr-Gb gain imbalance. The problem with this method is that it is not compatible with many CFA interpolation methods which interpolate RGB colors at the center of each cell. There are other CFA interpolation methods which are insensitive to Gr-Gb gain imbalance; however, use of these methods narrows the choice of CFA interpolation methods. This limits its application areas to certain ranges. Also, in some methods the sharpness of edges in images is reduced.